russell@unturf.com
094c19c745
cal_searching_got_instance_cb() scans entire search_hit_cache GSList with g_slist_find_custom() for each new calendar instance: O(1)+O(2)+...+O(N) = O(N^2). Fix: parallel GHashTable for O(1) membership test. 433x op-count speedup at N=1000. MOADs 0002/0003/0004/0005 CLEAN.
130 lines
3.8 KiB
Python
130 lines
3.8 KiB
Python
"""
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Unit test for evolution-0002: Calendar search_hit_cache dedup O(N^2) fix.
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Simulates cal_searching_got_instance_cb() collecting unique time_t values
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into a dedup'd cache. Measures op-count for defective (GSList linear scan)
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vs fixed (GHashTable O(1)) implementations.
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Asserts speedup > 3x at N=100 and N=1000.
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"""
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import time
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import sys
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PASS = True
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def collect_instances_defective(instances):
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"""
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Defective: linear scan for each new instance.
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Equivalent to g_slist_find_custom on a growing GSList.
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Returns (cache_list, op_count).
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"""
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cache = []
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ops = 0
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for val in instances:
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# Scan entire cache for duplicate — O(k) per insert
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found = False
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for c in cache:
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ops += 1
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if c == val:
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found = True
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break
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if not found:
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cache.append(val)
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return cache, ops
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def collect_instances_fixed(instances):
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"""
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Fixed: O(1) hash set membership check.
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Equivalent to g_hash_table_contains before g_slist_append.
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Returns (cache_list, op_count).
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"""
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cache = []
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cache_set = set()
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ops = 0
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for val in instances:
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ops += 1 # One hash lookup per insert
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if val not in cache_set:
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cache_set.add(val)
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cache.append(val)
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return cache, ops
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def run_test(label, n_instances, expected_speedup):
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global PASS
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# Generate N unique timestamps (weekly recurring event instances)
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# Mix in some duplicates: same event across two calendars = same timestamp
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unique = list(range(n_instances))
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# 20% duplicates simulating overlapping calendar subscriptions
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duplicates = unique[: n_instances // 5]
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instances = unique + duplicates
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# Shuffle deterministically for reproducibility
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# (keep order as-is; order doesn't change big-O result)
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_, ops_defective = collect_instances_defective(instances)
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cache_fixed, ops_fixed = collect_instances_fixed(instances)
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ratio = ops_defective / max(ops_fixed, 1)
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status = "PASS" if ratio >= expected_speedup else "FAIL"
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if status == "FAIL":
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PASS = False
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print(
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f"[{status}] {label}: N={n_instances} | "
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f"defective={ops_defective} ops | fixed={ops_fixed} ops | ratio={ratio:.1f}x "
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f"(expected >= {expected_speedup}x)"
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)
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# Verify correctness: both produce same unique set
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cache_defective, _ = collect_instances_defective(instances)
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if sorted(cache_defective) != sorted(cache_fixed):
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print(f" [FAIL] correctness: cache contents differ!")
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PASS = False
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else:
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print(f" correctness: caches match ({len(cache_fixed)} unique timestamps)")
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def run_wall_time_test(label, n_instances):
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"""Wall-clock speedup check for large N."""
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unique = list(range(n_instances))
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duplicates = unique[: n_instances // 5]
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instances = unique + duplicates
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t0 = time.perf_counter()
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collect_instances_defective(instances)
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t1 = time.perf_counter()
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collect_instances_fixed(instances)
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t2 = time.perf_counter()
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defective_ms = (t1 - t0) * 1000
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fixed_ms = (t2 - t1) * 1000
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ratio = defective_ms / max(fixed_ms, 1e-9)
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print(
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f" wall-clock {label}: defective={defective_ms:.2f}ms fixed={fixed_ms:.2f}ms ratio={ratio:.1f}x"
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)
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if __name__ == "__main__":
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print("evolution-0002: Calendar search_hit_cache dedup O(N^2) fix")
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print("=" * 65)
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run_test("N=50 op-count", 50, 3.0)
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run_test("N=100 op-count", 100, 10.0)
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run_test("N=500 op-count", 500, 50.0)
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run_test("N=1000 op-count", 1000, 100.0)
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print()
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run_wall_time_test("N=1000 wall-clock", 1000)
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run_wall_time_test("N=5000 wall-clock", 5000)
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print()
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if PASS:
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print("ALL TESTS PASSED")
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sys.exit(0)
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else:
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print("SOME TESTS FAILED")
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sys.exit(1)
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